Nursing Informatics Competency Assessment for Nurse Leaders (NICA-NL)

Transcription

1 Nursing Informatics Competency Assessment for Nurse Leaders (NICA-NL)

2 Speakers Mary Kennedy, MS, RN-BC Consultant, Adjunct Faculty (NEU) Sarah Collins PhD, RN; Senior Clinical and Nurse Informatician, Partners ecare, Partners HealthCare; Instructor in Medicine, Harvard Medical School and Brigham Health Andy Phillips PhD, RN Assistant Professor; MGH Institute of Health Professions Po-Yin Yen PhD, RN; Clinical Assistant Professor; The Ohio State University Stephanie Colman-Brochu MS, RN Manager Clinical Informatics at Milford Regional Medical Center

3 Mary K. Kennedy MS, RN-BC THE BEGINNING

4 Early EMR Adoption & Nurses HIMSS EMRAM Model Level 6 Level 7 Massachusetts 37 1 Rhode Island 7 0 New Hampshire 5 0 Conneticut

5 Resources for the Nurse Leader The Journey begins.

6 Resources for the Nurse Leader Vision ANCC Certification Nursing Informatics Develop Nursing Informatics Competency for the Nurse Leader (NICA-NL) Today s presentation Develop Nursing Informatics Competency for the Registered Nurse (NICA-RN) 2016 Parallel initiative: Mass. Action Coalition Nurse of the Future Nursing Core Nurse of the Future Core Nursing Competencies Updated 2016 Massachusetts Nurse of the Future Nursing Core Competencies (NOFNCC), the Toolkit Updated 2016 Conscious Decision not to focus on E.H.R. functional areas

7 Resources for the Nurse Leader Organization for Nurse Leaders (MA, RI, NH, CT) 2009 Established a Nursing Informatics and Technology as Management of Practice sub-committee Felt like trying to Boil the Ocean Focus on Membership surveys, Sharing best practices, Educational Opportunities:» guest speakers- national and local, partnerships with other professional organizations (i.e. HIMSS Nursing Informatics Institute) Competencies Recognized a need for an evaluation tool both Nurse Leaders and Registered Nurses (LPN-out of scope) Reached out to local experts which thought it was a good idea

8 Resources for the Nurse Leader Organization for Nurse Leaders (MA, RI, NH, CT) Understand needs, competency areas and develop a tool that can be delegated, or done independently and used broadly 2013 NICA- NL (Nursing Informatics Competency Assessment Nurse Leader) Delphi Study ONL Board and membership support IRB approved; Unfunded NICA- NL (Nursing Informatics Competency Assessment Nurse Leader)- Psychometric Analysis ONL Board and membership support IRB approved; Unfunded Snowball methodology to invite participants outside the New England area with the goal of developing a psychometric, valid and reliable tool Findings published in JONA

9 Sarah Collins PhD,RN THE DELPHI STUDY

10 Clinical informatics is not simply computers in medicine but rather is a body of knowledge, methods, and theories that focus on the effective use of information and knowledge to improve the quality, safety, and cost-effectiveness of patient care as well as the health of both individuals and populations. (Detmer DE, Shortliffe EH. Clinical Informatics. JAMA Published Online First: 13 May 2014.) Background Aim Informatics Competencies for Nursing and Healthcare Leaders (Westra and Delaney, 2008) "State of Contemporary Informatics Competencies for Chief Nurse Executives (Simpson, AONE 2013) HIT competencies require frequent attention and updating Rapid advances in technology Ensure relevance to nursing leaders' work To efficiently and comprehensively seek Nurse Leaders expert opinion of informatics competencies that are relevant & critical for a nurse leader to attain

11 Methods Clarify the concepts to measure Generate an item pool Determine the format for measurement Have the item pool reviewed by experts Consider inclusion of the validated items Administer items to a development sample Evaluate the items Optimize scale length Competency Identification Factor Analysis 11

12 Methods Data Collection Survey based on Westra and Delaney competencies + Simpson competencies Expert Delphi Survey 3 rounds Rounds 1 (June 2013 July 2013) Vote yes, include / no, exclude Enter free text comments Rounds 2 (Sep Oct 2013) Vote yes, include / no, exclude Review comments from round 1 Enter free text comments Rounds 3 (Dec Jan 2014) Rate on 4 point Likert scale» Not Relevant, Somewhat Relevant, Quite Relevant, Very Relevant (%) Review comments from round 2 Enter free text comments Changed competency wording based on comments from previous round

13 Methods Data Analysis Replicated methods from Westra & Delaney Content Validity Index (CVI; Polit and Beck, 2006) Criteria to retain a competency CVI >.80 votes of Quite or Very Important **CVI >.80 consistent with Westra and Delaney** Kruskal-Wallis ANOVA for differences among groups Qualitative analysis if borderline vote, can use qualitative data to make judgment if item should be retained Qualitative was not used to exclude items with CVI >.80

14 Participant Demographics (1/2) Number of Participants Round 1 Round 2 Round Percent that tool previous round NA Completed 1 st Round: 33.3% Completed 1st Round: 46.3% Completed 2 rd Round: 55%

15 Magnet Status 100% 90% 80% 70% 60% 50% 40% Round 1 Round 2 Round 3 30% 20% 10% 0% No Yes

16 Role 60% 50% 40% 30% 20% Round 1 Round 2 Round 3 10% 0% Staff Manager Director Executive Researcher OtherRole

17 Organization Type 60% 50% 40% 30% 20% 10% 0% Round 1 Round 2 Round 3-10%

18 Years Experience 70% 60% 50% 40% 30% Round 1 Round 2 Round 3 20% 10% 0% 3-5 years 6-10 years years years more than 25 years

19 Highest Degree 90% 80% 70% 60% 50% 40% 30% Round 1 Round 2 Round 3 20% 10% 0% Associates Degree Bachelors Degree Masters Degree Doctoral Degree

20 70% HIT Knowledge Compared to Peers 60% 50% 40% 30% Round 1 Round 2 Round 3 20% 10% 0% Below Average Above average Average

21 HIT Training 70% 60% 50% 40% 30% 20% Round 1 Round 2 Round 3 10% 0% Formal education* On the job training* Self-learner* None* Other* *Not mututally exclusive

22 Delphi Rounds Results Overview 108 competencies Round 1 98 competencies retained 10 competencies excluded Round 2 92 competencies retained 6 competencies excluded Round 3 74 competencies retained 18 competencies excluded

23 Categories of Competencies Retained in Round 3 Results (CVI>.80) Category Competencies Retained Management Concepts 9 Requirements and System Selection 9 Ethical/ Legal Concepts 8 Information Systems Concepts 7 Advances Software Applications 6 Executive Leadership 5 Financial 5 Implementation/ Management 5 Patient Related Applications 5 Data Issues 4 Technical knowledge 4 Collaboration 2 Electronic Communications 2 HIT Selection 2 Standardization 1 Total 74

24 Top 15 Ranked Competencies To Retain Not Relevan t (%) Somewhat Relevant (%) Quite Releva nt (%) Very Relev ant (%) CVI Ability to assure that Nursing values/ requirements are represented in HIT selection and 1 evaluation Inclusion of nursing information within HIT systems Budgeting using technology Data-based planning and decision making through the utilization and synthesis of HIT system 4 data Ability to collaborate with other departments regarding project management and resource 5 allocation for HIT system implementations Ability to collaborate with CMO peers related to HIT and needs of nurses and physicians Ability to collaborate with interprofessional team in HIT system selection process Ability to advocate for the development (or purchase) and use of integrated, cost-effective HIT 8 systems within the organization Communicating a system and nursing vision about the benefits of HIT Ability to involve front-line staff in the evaluation of HIT systems related to their practice Abilty to involve front-line staff in the development of HIT system requirements Ability to involve front-line staff in appropriate aspects of HIT design, implementation, and 12 testing related to their practice Ability to see HIT as a top priority and strategic decision Recognition of value of clinicians involvement in all appropriate phases of HIT Quality assurance using technology

25 Andy Phillips PhD, RN NICA-NL (METHODS)

26 Methods Clarify the concepts to measure Generate an item pool Determine the format for measurement Have the item pool reviewed by experts Consider inclusion of the validated items Administer items to a development sample Evaluate the items Optimize scale length Competency Identification Factor Analysis 26

27 Collection Methods for Inclusion and Validation of Competency Items Goal 1 - Consolidation of like items Multi-voting method* Eliminate potentially duplicative items Eliminated items can be added back later Facilitated process with experts Goal 2 Item Voting Each expert participant allocated votes ~1/2 of total items Voting using survey tool Prioritization or Elimination based on voting results Result Reduced list with high level of agreement *Nelson, E. C., Batalden, P. B., & Godfrey, M. M. (2011). Quality by design: a clinical microsystems approach: John Wiley & Sons.

28 Collection Methods for Inclusion and Validation of Competency Items Multi-voting method Goal 1 - Consolidation and clarification Repeat for all items Potential Competency Item 1 Is the meaning of the item clear? Edit language as needed Potential Competency Item 2 Is the meaning of the item clear? Edit language as needed Is the item the same as a prior item? 1. Consolidate 2 items Edit language as needed 2. Keep Item

29 Collection Methods for Inclusion and Validation of Competency Items Multi-voting method Goal 2 Item Voting Repeat Start with consolidated list from Step 1 (74 ->> 50 Items) Each expert participant allocated votes ~1/2 of total items Voting using survey tool Prioritization or Elimination based on voting results Evaluate prioritization Add back in items if necessary to reflect competency 50 ->> 45 Remaining Items across 12 categories

30 Collection Methods for Inclusion and Validation of Competency Items Administer Items to a Development Sample 1. Survey using 45 competency items (reflects reduction from 74 original items) 2. Sample of Nurse Leaders using snowball sampling methodology 3. IRB Approval

31 Po-Yin Yen PhD, RN PSYCHOMETRIC ANALYSIS

32 Methods Clarify the concepts to measure Generate an item pool Determine the format for measurement Have the item pool reviewed by experts Consider inclusion of the validated items Administer items to a development sample Evaluate the items Optimize scale length Competency Identification Factor Analysis 32

33 Nursing Informatics Competency Assessment for the Nurse Leader (NICA-NL): Instrument Refinement, Validation, and Psychometric Analysis Po-Yin Yen, PhD, RN

34 Exploratory Factor Analysis 1. searches for common clusters; 2. distinguish between clusters; 3. identify and eliminate irrelevant or indistinct (overlapping) items. 34

35 Procedures Determine the number of factors (e.g. parallel analysis, Veciler s MAP, eigenvalue-greater-than-one rule, model fit indices) Select extraction method (e.g. Principal Axis Factoring, Maximum Likelihood), and rotation types, (e.g. orthogonal- varimax vs. oblique rotations-promax) Item reductions based upon item loadings and Cronbach s alpha reliabilities Cross-loading: a).32 or higher on two or more factors; b) less than half the difference of factor loading with other factors Cronbach s alpha reliabilities Repeat procedures until final solution is reached

36 Results

37 Responses 539 responses 357 valid responses with < 20% missing values

38 Missing Data Imputation 357 valid responses with <20% missing values 216 responses had no missing values Impute missing values: Expectation Maximization (EM) Roth, P. L. (1994). Missing data: A conceptual review for applied psychologists. Personnel Psychology, 47, Gabriel L. Schlomer, Sheri Bauman, and Noel A. Card. Best Practices for Missing Data Management in Counseling Psychology Journal of Counseling Psychology 2010, Vol. 57, No. 1, 1 10 Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annu. Rev. Psychol., 60, Weaver, B., & Maxwell, H. (2014). Exploratory factor analysis and reliability analysis with missing data: A simple method for SPSS users. The Quantitative Methods for Psychology, 10 (2),

39 Number of Factors Eigen-value-greater-than-one rule Parallel analysis Model fit indices

40 Eigen-value-greater-than-one rule 40

41 Parallel Analysis Ledesma, Rubén Daniel and Pedro Valero-Mora (2007). Determining the Number of Factors to Retain in EFA: An easy-to-use computer program for carrying out Parallel Analysis. Practical Assessment Research & Evaluation, 12(2). O'Connor, B. P. (2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer's MAP test. Behavior Research Methods Instruments & Computers, 32(3), Turner, N. E. (1998). The effect of common variance and structure pattern on random data eigenvalues: Implications for the accuracy of parallel analysis. Educational and Psychological Measurement, 58(4), Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research. Educational and Psychological Measurement, 66(3),

42 Model Fit Indices Preacher, Kristopher J.; Zhang, Guangjian; Kim, Cheongtag; Mels, Gerhard. Choosing the Optimal Number of Factors in Exploratory Factor Analysis: A Model Selection Perspective. Multivariate Behavioral Research, v48 n1 p

43 Number of Factors Parallel analysis: 6 factors Eigen-value-greater-than-one rule: 5 factors Model fit indices: 6-7 factors Assess factor solutions with 5-7 factors

44 Extraction Method & Rotation Principal Axis Factoring (PAF) with oblique rotation (promax) as the extraction method In social and behavioral science, we usually expect some correlation among factors. With orthogonal (varimax) rotation, it may lose information if factors correlate. 44

45 Factor Solutions Comparison 45

46 Final 6-factor Solution (26 items) 1. Strategic Implementation Management (10 items) 2. Advanced Information Management and Education (5 items) 3. Executive Planning (4 items) 4. Ethical and Legal Concepts (2 items) 5. Information Systems Concepts (3 items) 6. Requirements and System Selection (2 items)

47 Factor Correlation Matrix Factor Strategic Implementation Management Advanced Information Management and Education Executive Planning Ethical and Legal Concepts Information Systems Concepts Requirements and System Selection Cronbach s alphas (α) were.96,.91,.90,.83,.92, &.81.

48

49 Feedback 50 comments Length: the survey [45 items] is too long Difficult language and terminology [NICA-NL] really reveals how deficient I am in this area- need much more education on this I do not speak this language

50 Nursing Informatics Education and Training Nursing informatics education and training are needed. Conceptually and theory wise I understand quite a bit, but resource wise have not been able to fully implement my role as an Informatics Nurse. I am currently in an MSN program taking Informatics. I really hope that you can implement informatics into the undergraduate level and also work to give older nurses an opportunity to learn this. I have only been in my role as a clinical nurse manager for 2 months. I have not been exposed to many of these concepts as a staff nurse. I believe with further education and training I can become competent in area related to Nursing Informatics.

51 Self-assessment vs. EHR assessment Some competencies are related to the capabilities of their EHR system, but an informatics nurse may not be able to implement with limited system functionalities or resources. [NICA-NL] may want to consider participants' understanding of HIT issues vs. actually implementing them The biggest challenge has been to make sure that the system meets the actual needs. Huge amounts of customization were necessary for our Cerner product and because of this the roll out was very slow and continues to pose challenges around effective documentation and our ability to retrieve aggregate information despite having an electronic record

52 Conclusion This research provides a foundation and focus for specific informatics and technology competencies required by today s Nurse Executive and Leader. This study established a valid and reliable nursing informatics competency assessment instrument, NICA-NL, for nurse leaders. Future direction includes advancing NICA-NL (additional analyses with more responses from other nurse leaders).

53 Stephanie Colman Brochu DNP (c), MS, RN-BC NICA-NL: UMMHC CASE STUDY

54 Introduction In the last decade, technology has touched all aspects of our society and has transformed the way we live, work, and communicate. Technology is embedded in almost every aspect of healthcare Information revolution 54

55 Importance of Nursing commitment Nurses have closest and most sustained relationships with patients and are largest users of technology Informatics and technology are integral tools built into all levels and areas of nursing practice 55

56 Local Problem UMass Memorial Health Care (UMMHC) is in the process of implementing an integrated electronic medical record across its enterprise 700 million dollar (projected) investment Data was lacking on nurse leaders informatics competencies prior to implementing the new EMR 56

57 The purpose of this study To examine the nurse leaders self report of competency in informatics To provide data to inform practice improvement needs in informatics competencies 57

58 Research Questions I. Do the nurse leaders have the informatics competencies needed to use a new electronic health record in a large academic medical center? II. How prepared are nurses in leadership positions to utilize information technologies to collect and analyze data to make business and patient care decisions? III. Does a relationship exist between groups and their self-reported competency in informatics? IV. Do differences exist between nurse leaders self-report of informatics competency between age, years in position, education, or years of experience? 58

59 Ethical Considerations IRB approval granted Expedited review Minimal risk Data confidential Reported at aggregate level 59

60 Methods Design Cross sectional, descriptive study Population Convenience sample; surveyed N=147 Inclusion criteria Exclusion criteria Setting: Four campuses of UMMHC Recruitment Invitation to participate Survey link Data collection 3 weeks period Analysis SPSS Statistics Descriptive Chi squared 60

61 Results Respondents Fifty-five nurse leaders completed the survey, response rate of 37% 61

62 Sample Characteristics Role N (%) Clinical Coordinator/Supervisor 10 (18.2) Clinical Nurse Educator 12 (21.8) Clinical Nursing Specialist 1 (1.8) Director 9 (16.3) Nurse Manager 20 (36.4) Other 3 (5.5) 62

63 Sample characteristics cont. Mean (SD) N (%) Missing N (%) Age 50.6 (11.9%) (3.6) (18.2) (12.7) (30.9) (27.3) 4 (7.3) Education N (%) BA/BS/BSN 10 (18.2) MA/MS/MPH 37 (67.3) PhD/DNP 3 (5.5) RN 5 (9.1) 63

64 Sample characteristics cont. Mean (SD) N (%) Years of experience 25.3 (5.24) <10 10 (18.2) (25.2) (18.2) (38.2) Years in position 5.5 (5.24) (58.2) (29.1) (12.7) 64

65 Results -> Nurse leaders overall reported being very competent in a number of competencies Factor Competency Very Competent Ethical & Legal Concepts Understanding of ethical principles for collection, maintenance, use and dissemination of data and information related to HIT 50.9% Requirements & System Selection Strategic Implementation Management Understanding of patients rights related to HIT and computerized patient data Ability to assure that nursing values/requirements are represented in HIT selection & evaluation A conceptual understanding of nursing intervention documentation using HIT, its impact on care delivery, nursing productivity and secondary use of information Conceptual understanding of the importance of integrating nursing data elements into HIT systems 58.5% 49.1% 54.5% 54.5% Communication a system and nursing vision about the benefits of HIT 54.7% Recognition May 10, 2017 of value of clinicians involvement in all appropriate 69.8% 65 phases of HIT

66 Results-> Nurse leaders reported less competency in a number of competencies Factor Competency Less competent Executive Planning In the ability to define (in collaboration with IT department) the Total Cost of Ownership (TCO) containment strategies and hidden costs of HIT implementation 56.2% In the ability to define (in collaboration with the IT department) TCO related to the HIT related cost of staff education and re-education due to upgrades and staff turnover 48% Information Systems Concepts Strategic Information Management In the ability to understand how to define, design, and implement a HIT solution for nursing workflows Understanding of methods for evaluation of HIT implementation and use 46.3% 40.4% Advanced Information Management and Education Conceptual understanding of data quality issues for HIT 38.9% 66

67 Results No differences existed by education level or years in position. Differences existed between reported informatics competencies by: Age, years working experience and professional position. 67

68 Age Nurse leaders 60 years and older reported more competency than their counterparts in the ability to understand the ethical principles for the collection, use, and dissemination of data and information related to HIT (x 2 =36.48, p=.002). 68

69 Position Senior executive nurse leaders reported more competency than less senior leaders in: Ability to define the TCO specifically associated with education, reeducation and turnover (x 2 = , p=.002) Conceptual understanding of data quality issues related to HIT (x 2 =20.443, p=.0009) Ability to understand regulations and transitions in policies as they relate to HIT policy requirements (x 2 = , p=.03) 69

70 Years of experience Nurse leaders with 31+ years of experience reported greater competency in Understanding of ethical principles for collection, maintenance, use & dissemination of data & information (x 2 =18.928, p=.02) Recognition of value of clinicians involvement in all appropriate phases of HIT (x 2 =18.888, p=.02) 70

71 Years of experience cont. Nurse leaders with <10 years of experience reported greater competency in Communicating a systems and nursing vision about the benefits of HIT (x 2 =16.899, p=.05) Ability to champion the collection, analysis and trending of nursing data in non-nursing dominated HIT discussions (x 2 =21.235, p=.04) Ability to manage the impact of change due to HIT implementation (x 2 =18.096, p=.03) Ability to evaluate, contribute and revise project scope, objectives and resources (x 2 = , p=.03) 71

72 Discussion Nurse leaders at UMMHC have many of the informatics competencies needed to utilize the new EHR to support quality patient care and fiscal decision making. Senior nurse executives and nurse leaders with a number of years of experience reported greater competency in several areas. Nurse leaders with less experience reported greater competency in ability to manage change, to communicate a nursing vision and in representing data specific to nursing. Several areas were identified as professional development opportunities to enhance informatics knowledge and skill. 72

73 Conclusions Information technology is the stethoscope of the 21 st century (TIGER) Nurse leaders are better prepared than 10 years ago but more needs to be done Technology changes rapidly Life cycle is ~ 7 years Further research is needed Continue validation of the instrument 73

74 Limitations New instrument Small sample size One academic center, not generalizable The organization was in process of designing/building their EMR so awareness may have been heightened 74

75 NEXT

76 NICA NICA-NL Scoring metric Further analysis / trends Deployment/ Implementation Partners NICA- RN Complete analysis, publish Funding Exploring Polarity Both-And Thinking Real Time Health System ( Gartner)

77 QUESTIONS JONA Nursing Informatics Competency Assessment for the Nurse Leader: The Delphi Study Nursing Informatics Competency Assessment for the Nurse Leader: Instrument Refinement, Validation, and Psychometric Analysis